Using test workload run facts and problem discovery data as input for business analytics to determine test effectiveness

ABSTRACT

Aspects of the present invention include a method, system and computer program product for utilizing various data to determine the effectiveness of a software testing procedure. The method includes preloading, by a processor, into a database data related to workloads and workload data, data related to analysis points, and customer data. The method also includes determining, by the processor, whether to modify a workload model, to run a workload or to perform one of post processing analytics or run time analytics. The method further includes performing, by the processor, one of post processing analytics or run time analytics of the data preloaded into the database.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of U.S. patentapplication Ser. No. 15/197,831 filed on Jun. 30, 2016, the contents ofwhich are incorporated herein by reference.

BACKGROUND

The present invention relates to the testing of software, and morespecifically, to a method, system and computer program product thatimplement aspects of workload and operational profiling, coupled withbusiness analytics, thereby resulting in improvements in the testing ofcustomer software.

In the field of software testing, as in many other technical fields,improvements are constantly being sought, primarily for cost andaccuracy reasons. A fundamental goal of software testing in theory is toidentify all of the problems in a customer's software program before theprogram is released for use by the customer. However, in reality this isfar from the case as typically a software program is released to thecustomer having some number of problems that were unidentified duringthe software development and testing process.

A relatively more proactive approach to improving software testing issought that employs traditional methods of understanding characteristicsof clients' environments, augmented with a process of data miningempirical systems data. Such client environment and workload profilinganalysis may result in software test improvements based oncharacteristics comparisons between the client and the testenvironments.

SUMMARY

According to one or more embodiments of the present invention, acomputer-implemented method includes preloading, by a processor, into adatabase data related to workloads and workload data, data related toanalysis points, and customer data. The method also includesdetermining, by the processor, whether to modify a workload model, torun a workload or to perform one of post processing analytics or runtime analytics. The method further includes performing, by theprocessor, one of post processing analytics or run time analytics of thedata preloaded into the database.

According to another embodiment of the present invention, a systemincludes a processor in communication with one or more types of memory,the processor configured to preload into a database data related toworkloads and workload data, data related to analysis points, andcustomer data. The processor is also configured to determine whether tomodify a workload model, to run a workload or to perform one of postprocessing analytics or run time analytics. The processor is furtherconfigured to perform one of post processing analytics or run timeanalytics of the data preloaded into the database.

According to yet another embodiment of the present invention, a computerprogram product includes a non-transitory storage medium readable by aprocessing circuit and storing instructions for execution by theprocessing circuit for performing a method that includes preloading, bya processor, into a database data related to workloads and workloaddata, data related to analysis points, and customer data. The methodalso includes determining, by the processor, whether to modify aworkload model, to run a workload or to perform one of post processinganalytics or run time analytics. The method further includes performing,by the processor, one of post processing analytics or run time analyticsof the data preloaded into the database.

Additional features and advantages are realized through the techniquesof the present invention. Other embodiments and aspects of the inventionare described in detail herein and are considered a part of the claimedinvention. For a better understanding of the invention with theadvantages and the features, refer to the description and to thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofthe invention are apparent from the following detailed description takenin conjunction with the accompanying drawings in which:

FIG. 1 depicts a cloud computing environment according to one or moreembodiments of the present invention;

FIG. 2 depicts abstraction model layers according to one or moreembodiments of the present invention;

FIG. 3 is a block diagram illustrating one example of a processingsystem for practice of the teachings herein;

FIG. 4 is a flow diagram of a method for utilizing various data todetermine the effectiveness of a software testing procedure inaccordance with one or more embodiments of the present invention; and

FIG. 5 is a block diagram of a system having two databases that storevarious types of data for use with the method of FIG. 4 and inaccordance with one or more embodiments of the present invention.

DETAILED DESCRIPTION

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and a method 96 for utilizing various data todetermine the effectiveness of a software testing procedure inaccordance with one or more embodiments of the present invention.

Referring to FIG. 3, there is shown a processing system 100 forimplementing the teachings herein according to one or more embodiments.The system 100 has one or more central processing units (processors) 101a, 101 b, 101 c, etc. (collectively or generically referred to asprocessor(s) 101). In one embodiment, each processor 101 may include areduced instruction set computer (RISC) microprocessor. Processors 101are coupled to system memory 114 and various other components via asystem bus 113. Read only memory (ROM) 102 is coupled to the system bus113 and may include a basic input/output system (BIOS), which controlscertain basic functions of system 100.

FIG. 3 further depicts an input/output (I/O) adapter 107 and a networkadapter 106 coupled to the system bus 113. I/O adapter 107 may be asmall computer system interface (SCSI) adapter that communicates with ahard disk 103 and/or tape storage drive 105 or any other similarcomponent. I/O adapter 107, hard disk 103, and tape storage device 105are collectively referred to herein as mass storage 104. Operatingsystem 120 for execution on the processing system 100 may be stored inmass storage 104. A network adapter 106 interconnects bus 113 with anoutside network 116 enabling data processing system 100 to communicatewith other such systems. A screen (e.g., a display monitor) 115 isconnected to system bus 113 by display adaptor 112, which may include agraphics adapter to improve the performance of graphics intensiveapplications and a video controller. In one embodiment, adapters 107,106, and 112 may be connected to one or more I/O busses that areconnected to system bus 113 via an intermediate bus bridge (not shown).Suitable I/O buses for connecting peripheral devices such as hard diskcontrollers, network adapters, and graphics adapters typically includecommon protocols, such as the Peripheral Component Interconnect (PCI).Additional input/output devices are shown as connected to system bus 113via user interface adapter 108 and display adapter 112. A keyboard 109,mouse 110, and speaker 111 all interconnected to bus 113 via userinterface adapter 108, which may include, for example, a Super I/O chipintegrating multiple device adapters into a single integrated circuit.

In exemplary embodiments, the processing system 100 includes a graphicsprocessing unit 130. Graphics processing unit 130 is a specializedelectronic circuit designed to manipulate and alter memory to acceleratethe creation of images in a frame buffer intended for output to adisplay. In general, graphics processing unit 130 is very efficient atmanipulating computer graphics and image processing, and has a highlyparallel structure that makes it more effective than general-purposeCPUs for algorithms where processing of large blocks of data is done inparallel.

Thus, as configured in FIG. 3, the system 100 includes processingcapability in the form of processors 101, storage capability includingsystem memory 114 and mass storage 104, input means such as keyboard 109and mouse 110, and output capability including speaker 111 and display115. In one embodiment, a portion of system memory 114 and mass storage104 collectively store an operating system to coordinate the functionsof the various components shown in FIG. 3.

In accordance with one or more embodiments of the present invention,methods, systems, and computer program products are disclosed forutilizing various data to determine the effectiveness of a softwaretesting procedure in accordance with one or more embodiments of thepresent invention.

One or more embodiments of the present invention allow an organizationto better allocate its software test resources to provide for relativemaximum productivity and value. Multiple system level test teams withinan organization often run the same workload for a given softwarecomponent, many times in the same configuration or model. Some testteams run these workloads frequently and others run them infrequently.Some test teams are relatively focused and diligent and run theworkloads at high stress levels, while other test teams run theworkloads at much lower stress levels and without much focus. As aresult, some of the test teams are relatively more productive at findingdefects in the software than others. Yet, it is almost always true thatan organization's test resources are limited and risks must be assessedand taken.

Further, it is not at all uncommon to run tests and workloads that arenot productive. Even with the best intentions, without empirical data toeither confirm or deny the value of a test to the business, it isdifficult to fund and manage a large test operation and know that theinvestment funds are used to get the best possible results. In thetesting of customer software, results mean finding defects that thecustomer is likely to encounter, not just defects that can be found.Accordingly, embodiments of the present invention provide a method fordetermining, from a test investment perspective, how an organization canbetter spend its limited test dollars to yield the highest overallproductivity and best return on its investment.

In addition, one or more embodiments of the present invention utilizeundisputable facts, or workload run facts and problem discovery facts,to better ensure that accurate and factual information is used todetermine an organization's investment in the software testing process.This is advantageous because systems level tests typically requireexpensive test system resources and highly skilled personnel.

Further, one or more embodiments of the present invention utilize a bodyof available customer data, test workload execution run facts data, anddefect tracking data together with big data analytics to assist in abetter understanding of the confirmation or denial of value of anorganization's testing procedures. Not only may defect effectiveness beused as a criterion, but also workload variance is a relatively goodmeasure of test effectiveness.

Referring now to FIG. 4, there illustrated is a flow diagram of a method200 for utilizing various data to determine the effectiveness of asoftware testing procedure. Referring also to FIG. 5, there illustratedis a block diagram of a system 300 having two databases (“DB2”) 304, 308that store various types of data for use with the method 200 of FIG. 4and in accordance with one or more embodiments of the present invention.

In exemplary embodiments, the functional features of the method 200 ofFIG. 4 may be executed by computing or processing components within thecloud computing environment 50 of FIG. 1, or in the processing system100 of FIG. 3, or in some other type of computing or processingenvironment. Also, the two databases 304, 308 may be implemented withinthe cloud computing environment 50 of FIG. 1, or in the processingsystem 100 of FIG. 3, or in some other type of computing or processingenvironment.

After a starting operation in block 204, blocks 208, 212 and 216 operateto store workloads and workload data in the database 304. The workloadrun facts database schema groups test run time data around a run factsID. The run facts ID ties all of the preloaded schema data 208 with therun time workload and systems feedback data 232, 236 that makes up thebig data portion of the data store in the database 304.

The preloaded data 208, 212, 216 includes data detailing the eligibletest bed environments, the systems in the environments, specific testersin the environments, workload metadata including components under testand models, the analysis point groups and their analysis pointscontainers of data points, the system configs or setup data, etc.

Once the data has been preloaded in the operations 208, 212, 216, themethod 200 executes a decision operation in a block 220 which choosesfrom a model path that modifies the workload model in the block 224, acontrol path 228, 232 and 236 that runs a workload and collects andstores workload and systems runfacts therefrom, or a path 240 thatallows for one of post processing analytics or run time analytics of thedata using analytics tools. After the post processing or run timeoperation 240, the method 200 may then end in the block 244.

The run time data stored in the database 304 may include, for example,the workload controls, workload feedback, system feedback, systemalerts, system errors, and workload errors. The run time data along withthe preloaded data make up the body of runfacts that can be analyzed inthe block 240 by post processing or run time analyzing the data usinganalytic tools such as, for example, analytic, modeling, cognitive, andreporting products to perform the analysis. The analysis performed inthe operation 240 includes tying in all of the data down to the testerwho ran the test and that tester's history to score multiple aspects ofthe runfacts such as, test to customer cluster proximity, test tocustomer activity intersection, most defect removing and productiveworkloads, environments, systems, testers, etc.

The post processing or run time analysis data includes the runfacts datastore stored in the database 304 and test's defect tracking data storerecords 320 as stored in the database 308. Using both sets of datatogether in the near real time (i.e., run time) analysis or in the postprocessing analysis allows multidimensional analytics to be applied tohelp an organization understand the value of its tests, workloads,environments, testers, etc., to the organization.

In addition, once the workload run facts have been collected formultiple test teams and the various test environments, theaforementioned analytic, modeling, cognitive, and reporting products maybe used in the block 240 to generate business analytics reports. Thesereports can be used to guide informed test business decisions aboutwhich test teams, which of their multiple test environments, and whichworkload models are most productive at finding defects. The workloadconfiguration or model, the workload feedback, and the system feedbackrun facts can be correlated with problem discovery data to indicate testworkload configuration or model, and test team and their multiple testenvironments effectiveness. This allows for grading of test efforts andtheir individual test environments regarding workload run participationand success or productivity based on 30, 60, 90, and greater than 90 daycategories. Also, grading may be performed using a release by releasescope. This grading information can be used along with problem discoveryfor each test team such as integration test and system test. Then withinthe overall scope of the test teams and their various test beds such asSystem Test's IST, DFSMS test, Combat, general SVT or IntegrationTest's, IT, Service Test, etc. use the analysis data to provide acorrelation of workload run facts with problem discovery. Provide agrade from 0 to 100 to indicate the value of the test teams'participation in certain testing to determine the business value. Thisinformation may be used, for example, to invest in productive tests anddivest in the tests that are not productive.

One or more embodiments of the present invention also provide thecapability to add variability to workloads. This solves the problem ofrunning the same workload, the same way, over and over again in multipletest teams in their various test environments. If the only variant ofthe repeated test runs with the same configuration is when new ormodified code is dropped into the test, then once the code is availablefor test, running the same workload configuration or model over and overagain and expecting is different result does not instill greatconfidence with regard to problem discovery.

Instead, one or more embodiments of the present invention involve testcollecting workload run facts each time a workload is exercised in anyof the available test environments. These workload run facts include theworkload configuration or model information, workload feedback data, andsystem feedback data. The workload feedback data includes informationsuch as the transaction mix or workload model, number of simulatedusers, the transaction rate, the failure rates per transaction, and thenumber and distribution of errors and failures. The system feedback dataincludes the empirical systems data that shows how the target componentis being exercised with regard to functional activity as well as levelsof load and stress. The empirical system data is comprised ofperformance and accounting data that is readily available in the system(e.g., Z) operating environment.

In addition, one or more embodiments of the present invention utilize aworkload portal concept to develop test tooling. As a component of aworkload portal, a DB2 database 304 may be created that is compatiblewith the analytics tool requirements. In this database 340, workload runfacts data 312 is stored that includes the workload configuration modelinformation. This includes the transaction or thread type mix anddistribution of the transactions or threads. The workload model can bethought of as a mixer of sorts. If a workload has two transactions orthreads, transaction A and transaction B, one can run the workload withan equal distribution for the two transactions. Thus, as the workloadsimulation runs, the transactions are executed with an equaldistribution. Over the workload run each transaction may, for example,be run 100 times each. However, if the distribution were modeled ormixed to run transaction A with a distribution of one and transaction Bwith a distribution of five, then transaction B would be run five timesfor every time transaction A were run.

The model or mix can be stored in the DB2 304 as part of the workloadrun facts data. This model or mix is one part of the input to theanalytics operation 240 and may, on its own, be used to create a usefulanalysis to determine workload model to defect discovery correlation.Additionally, if one includes workload feedback in the form of number ofsimulated users, transaction rate, total errors, and error distribution,then one can provide additional input into the analytics so that thereis also an understanding of specific workload run time characteristicsthat correlate to productive defect discovery.

Additionally, the system feedback can also be used as input to theanalytics 240 to enable understanding of which system pressure pointscorrelate to productive defect discovery. All of the aforementionedworkload run facts help to build an historical record of all testing forthe given workload and target component(s).

In other embodiments, this historical data may be used in a similarapproach to determine the best test team and test environment to trustwith critical situation tests and recreate where time to resolution is amajor business concern.

One or more embodiments of the present invention provide a solution thatincludes methods, systems and computer program products for atraditional processing environment or a cloud computing environmentbased set of exploitable services that make up a test workload executiondiary that includes customer data contrasting and comparison. Allinformation needed for analysis is brought together in a test controland data collection model called workload portal. The workload portal isa link between the test operator, the test system, and the test data.The services include workload modeling so that variation for workloadscan be accomplished, system configuration so that workload and systemsetup can be varied, workload control to increase load, alter theexecution model, provide visual workload feedback such as rates anderrors, visual system feedback with customer data convergencevisualization, system alerts for out of convergence issues, and allwhile making a diary of the workload run.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork, including a local area network (LAN) or a wide area network(WAN), or the connection may be made to an external computer (forexample, through the Internet using an Internet Service Provider). Insome embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements but can include other elements not expressly listed or inherentto such composition, mixture, process, method, article, or apparatus.

As used herein, the articles “a” and “an” preceding an element orcomponent are intended to be nonrestrictive regarding the number ofinstances (i.e., occurrences) of the element or component. Therefore,“a” or “an” should be read to include one or at least one, and thesingular word form of the element or component also includes the pluralunless the number is obviously meant to be singular.

As used herein, the terms “invention” or “present invention” arenon-limiting terms and not intended to refer to any single aspect of theparticular invention but encompass all possible aspects as described inthe specification and the claims.

As used herein, the term “about” modifying the quantity of aningredient, component, or reactant of the invention employed refers tovariation in the numerical quantity that can occur, for example, throughtypical measuring and liquid handling procedures used for makingconcentrates or solutions. Furthermore, variation can occur frominadvertent error in measuring procedures, differences in themanufacture, source, or purity of the ingredients employed to make thecompositions or carry out the methods, and the like. In one aspect, theterm “about” means within 10% of the reported numerical value. Inanother aspect, the term “about” means within 5% of the reportednumerical value. Yet, in another aspect, the term “about” means within10, 9, 8, 7, 6, 5, 4, 3, 2, or 1% of the reported numerical value.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method comprising:preloading, by a processor, into a database data related to workloadsand workload data, data related to analysis points, and customer data;determining, by the processor, whether to modify a workload model, torun a workload or to perform one of post processing analytics or runtime analytics; and performing, by the processor, one of post processinganalytics or run time analytics of the data preloaded into the database.2. The computer-implemented method of claim 1 wherein determining, bythe processor, whether to modify a workload model, to run a workload orto perform one of post processing analytics or run time analyticsfurther comprises the processor modifying a workload model.
 3. Thecomputer-implemented method of claim 1 wherein determining, by theprocessor, whether to modify a workload model, to run a workload or toperform one of post processing analytics or run time analytics comprisesthe processor running a workload.
 4. The computer-implemented method ofclaim 3 wherein the processor running a workload comprises the processorcollecting and storing workload data and system runfacts data.
 5. Thecomputer-implemented method of claim 4 further comprising performing, bythe processor, one of post processing analytics or run time analytics ofthe data preloaded into the database and of the stored workload data andthe system runfacts data.
 6. The computer-implemented method of claim 5wherein the processor performing one of post processing analytics or runtime analytics further comprises the processor generating businessanalytics reports.
 7. The computer-implemented method of claim 4 furthercomprising performing, by the processor, one of post processinganalytics or run time analytics of test defects data.